{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wTdUyy89bc+3g3gco3nVEa3XpOvN6lamxmW+MMcYYY4wxxhjjJnDrzXyj0RjK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27fQrtLqGA+V0fQp/myv9eE51G/yqUSxieJagyiEPMr56hfORuiR4hVqTyFOB49ryCNo4FASVsD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\nwZf+rter+PJs43zswjXu7x66d6W5tESjoPm2+gwl2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8e5fM3e525rzPl1EZeNYzdXZplh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\nQ4o/3TZ1Qqq9XNt0LQbbnuHIRhMKOWuaSFGI43NZBN6A1ESBvTL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k8CxVxC/+4i8BMB6N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55yJ+ATR04gaLl36uYfjeGgwK5wfX5n7T5u33SZQtub6yio/wPPw0d3aezFQ27j5MQEJkmb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\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kMheSNWZ26pnbBdLukY8XFjqXZ7afRxrRMd3+BD0um1CtN52Ea7R2dSY3N57cxr1O5HZd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TTanbt+bwo2F0VOoc+i293F83ydkzo9RXqKoqCmqOtkMiZN5VnHcVxef6+Wp0WJ4nueR6LImzFO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9650240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072)\n"
     ]
    }
   ],
   "source": [
    "print(classifier.X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_one_loop(X_test)\n",
    "print(dists.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3803.92350081]]\n"
     ]
    }
   ],
   "source": [
    "print(dists[0:1,0:1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n",
      "[[ 3803.92350081]]\n"
     ]
    }
   ],
   "source": [
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)\n",
    "print(dists[0:1,0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ojo7Y29vDbrdzfHwMwMTEBJ1Oh729Pfb29jCZTBiNRp48eSL7wOVykc1mOT09FVQrlUph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\niYiIiCRSZ0xEREQkkTpjIiIiIol+A1rafnUl9xe+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x59f9278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3803.92350081  4210.59603857  5504.0544147  ...,  4007.64756434\n",
      "  4203.28086142  4354.20256764]\n",
      "[ 420 3684 4224 ..., 4375 2744 4601]\n",
      "[ 2641.43408019  2707.54796818  2753.57930701  2785.19819762  2806.03118301]\n",
      "[4 4 4 6 6]\n",
      "{4: 3, 6: 2}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_temp = dists[0,:]\n",
    "print(dis_temp)\n",
    "sortedDistIndicies = dis_temp.argsort()\n",
    "print(sortedDistIndicies)\n",
    "closet_ind = sortedDistIndicies[0:5]\n",
    "print(dis_temp[sortedDistIndicies[0:5]])\n",
    "print(y_train[closet_ind])\n",
    "set_label = set(y_train[closet_ind])\n",
    "y_pred_temp = {}\n",
    "for ii in set_label:\n",
    "     y_pred_temp[ii] = list(y_train[closet_ind]).count(ii) \n",
    "print(y_pred_temp)\n",
    "max(y_pred_temp,key=y_pred_temp.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your Answer**: *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 2**\n",
    "We can also other distance metrics such as L1 distance.\n",
    "The performance of a Nearest Neighbor classifier that uses L1 distance will not change if (Select all that apply.):\n",
    "1. The data is preprocessed by subtracting the mean.\n",
    "2. The data is preprocessed by subtracting the mean and dividing by the standard deviation.\n",
    "3. The coordinate axes for the data are rotated.\n",
    "4. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "\n",
    "*Your explanation*:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 7906696.077041\n",
      "Uh-oh! The distance matrices are different\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 22.097000 seconds\n",
      "One loop version took 91.898000 seconds\n",
      "No loop version took 0.000000 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     56\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-d17d2ce074e9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[0myte\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_train_folds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 45\u001b[1;33m         \u001b[0mXtr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXtr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m4\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     46\u001b[0m         \u001b[0mytr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mytr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m4\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m         \u001b[0mXte\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXte\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[1;34m(a, newshape, order)\u001b[0m\n\u001b[0;32m    230\u001b[0m            [5, 6]])\n\u001b[0;32m    231\u001b[0m     \"\"\"\n\u001b[1;32m--> 232\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'reshape'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    233\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;31m# a downstream library like 'pandas'.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36m_wrapit\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     45\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[0mwrap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     48\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# Your code\n",
    "\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k] = np.zeros(num_folds)\n",
    "    for i in range(num_folds):\n",
    "        Xtr = np.array(X_train_folds[:i] + X_train_folds[i+1:])\n",
    "        ytr = np.array(y_train_folds[:i] + y_train_folds[i+1:])\n",
    "        Xte = np.array(X_train_folds[i])\n",
    "        yte = np.array(y_train_folds[i])     \n",
    "\n",
    "        Xtr = np.reshape(Xtr, (int(X_train.shape[0] * 4 / 5), -1))\n",
    "        ytr = np.reshape(ytr, (int(y_train.shape[0] * 4 / 5), -1))\n",
    "        Xte = np.reshape(Xte, (int(X_train.shape[0] / 5), -1))\n",
    "        yte = np.reshape(yte, (y_train.shape[0] / 5, -1))\n",
    "\n",
    "        classifier.train(Xtr, ytr)\n",
    "        yte_pred = classifier.predict(Xte, k)\n",
    "        yte_pred = np.reshape(yte_pred, (yte_pred.shape[0], -1))\n",
    "        num_correct = np.sum(yte_pred == yte)\n",
    "        accuracy = float(num_correct) / len(yte)\n",
    "        k_to_accuracies[k][i] = accuracy\n",
    "\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 1\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 3**\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The training error of a 1-NN will always be better than that of 5-NN.\n",
    "2. The test error of a 1-NN will always be better than that of a 5-NN.\n",
    "3. The decision boundary of the k-NN classifier is linear.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "\n",
    "*Your explanation*:"
   ]
  }
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